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3 Cards in this Set
- Front
- Back
Emipically Validated Treatment (ETV)/Empirically Supported Treatment (EST) |
Research integrated into practice |
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Correlation |
●Negative 1 to 0 to positive 1 ●0= No correlation ●-1 and +1 = Both are perfect correlation ●Positive Correlation - When X goes up, Y goes up (ex. When you study more, GPA goes up) ●Negative Correlation - When X goes up, Y goes down (ex. The more you brush your teeth, the less you will have cavities) |
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True Experiment |
●Two or more groups used ●Random Sampling ●Random assignment ●Systematic Sampling- Every nth person can also be used ●Quasi-experimental- Doesn't ensure causality, groups not random or researchers can't control the independent variables (IV) ●Experimental groups get the Independent Variables (IV)/experimental variables ●Control Groups do not receive the IV ●Dependent Variable (DV) - Outcome Data (ex. Do eating carrots raise IQ? Carrots = IV, DV = Measured IQ) ●Null Hypothesis - No significant difference between Control and Experimental groups ●Experimental/alternative hypothesis- There is a significant difference (carrots DO raise IQ) ●Type I alpha error - Researcher rejects null hypothesis that IS true (i.e. truly no significant difference, but research claims difference in experimental group- carrots don't raise IQ, but research claims they do) ●Type II beta error - Researcher accepts null when it should have been rejected (i.e. Research claims no difference, but there is - Research claims no significance between carrots and raised IQ, but there is a difference) ●Significance Level - 0.05 or less ●N=1 - Single subject design or case study ●Demand Characteristics - Evident when research subjects have cues about desired/undesired behavior, Can make research inaccurate. ●Obtrusive/reactive measure - Subjects know they are being observed (ex. Observer presence) ●Internal Validity - high when experimental has few flaws and findings are accurate. Low when researcher didn't measure what they thought they measured. ●External Validity - high when results can be generalized to other settings ●Confounding Extraneous Variables- other factor that causes change (ranch dressing carrots dipped in raised IQ) ●t Test - Parametric test comparing two means ●ANOVA/analysis of variance - 2 or more means to compare, provides F variables ●F test - Will tell you if significant differences are present ●MANOVA/multivariant analysis of variance- Use when more than one DV. ●Factorial analysis of variance- when investigating more than one IV ●Chi Quare or Kruskal-Wallace- Non-parametric tests for when a population is not necessarily normal ●Ex post facto/causal comparative design - researcher looking at after the fact data ●Descriptive Statistics - Describe central tendency like mean, median, mode, range, quartiles, variance, standard deviation ●Statistical Analyses - correlation coefficients, t tests, ANOVAs, analysis of covariance, Chi square, Kruskal-Wallace, etc. ●Cohort Studies - Exam groups of people with things in common ●Longitudinal Research - Relies on observation or data over a period of time ●Cross-sectional data - observations or data from a given point in time. ●Formative evaluation - Takes place during treatment or program ●Summative/Outcome Evaluation - takes place at the end of treatment or program ●Between Groups Design - Different subjects in different groups ●Within Groups Repeated Measures Design - Same subjects for control conditions and at a different time for experimental conditions ●Cauusal Comparative Design - |